KR102024949B1 - Efficient method and system for the acquisition of scene imagery and iris imagery using a single sensor - Google Patents

Abstract

The present disclosure relates to methods and systems for capturing images of an iris and a scene using a single image sensor. The image sensor may capture a view of the scene and a view of the iris within the at least one image. The image processing module may generate an image of the scene by applying a level of noise reduction to the first portion of the at least one image. The image processing module may apply the level of reduced noise reduction to the second portion of the at least one image to generate an image of the iris for use in biometric identification.

Description

Efficient METHOD AND SYSTEM FOR THE ACQUISITION OF SCENE IMAGERY AND IRIS IMAGERY USING A SINGLE SENSOR}

This application is filed on February 17, 2011, entitled "Method and System for Iris Recognition and Face Acquisition," US Provisional Application No. 61 / 443,757 and filed April 6, 2011, "Efficient Method and System for Claims the benefit of priority to US Provisional Application No. 61 / 472,279 entitled "The Acquisition of Scene Imagery and Iris Imagery using a Single Sensor", both of which are hereby incorporated by reference in their entirety for all purposes. do.

TECHNICAL FIELD The present invention relates to image processing techniques, and more particularly, to an efficient system and method for acquiring scene images and iris images using a single sensor.

Typically, biometric systems are designed to obtain optimal images by considering specific constraints of the biometric type. If other data (eg a face or background image) is to be obtained, different sensors are typically used because the requirements for different image types are very different. However, such an approach adds cost to the overall solution and can also increase the size or footprint of the system.

U.S. Patent Publication 20060050933 by Adam et al. Attempts to solve the problem of acquiring data for use in face and iris recognition using a single sensor, but the image is obtained so that the data obtained for each of the face and iris recognition components is individually optimal. It does not solve the problem of optimizing the acquisition.

US Patent Publication 20080075334 by Determan et al. And US Patent Publication 20050270386 by Saitoh et al. Disclose the acquisition of facial and iris images for recognition using individual sensors for faces and individual sensors for irises. Saitoh describes a method for performing iris recognition, which includes identifying the position of the iris using face and iris images, but using two separate sensors that are individually focused on each of the face and iris and displaying the data. Acquiring at the same time, user motion is irrelevant.

U.S. Patent Publication 20080075334 by Determan et al. Also discusses the use of one sensor for both face and iris, but the problem of optimizing image acquisition such that the data obtained for each of the face and iris recognition components is individually optimal. Does not solve.

US Patent Publication 20070206840 to Jacobson et al. Also describes a system that includes acquiring images of faces and irises, but the problem of optimizing image acquisition such that the data obtained for each of the face and iris recognition components is individually optimal. It does not solve the problem, and does not cover how to get a smaller system size.

In certain aspects, methods and systems are described herein for using a single sensor to obtain a high quality image of an iris for biometric identification, and a high quality picture of any other scene, such as a human face. Embodiments of these systems and methods employ a single sensor for the purpose of determining or verifying an individual's identity using biometrics using iris, as well as for obtaining a general image of scenes such as faces and places. Can be used to cause an image to be acquired. The latter type of image can typically be obtained by, for example, a mobile phone user. Thus, the disclosed methods and systems may be integrated into mobile and / or small devices. The sensor may be a complementary metal oxide semiconductor (CMOS) sensor or another suitable type of image capture device. The methods and systems can set or adjust conditions to be nearly optimal in two acquisition modes, such as an iris image acquisition mode and a picture (eg, non-iris) acquisition mode. In some embodiments, systems for obtaining such images, eg implemented within a device, may have a significantly reduced physical size or footprint compared to devices using multiple sensors, for example.

In one aspect, the present disclosure describes a method of obtaining images of an iris and a scene using a single image sensor. The method may include capturing a view of the scene and a view of the iris within the at least one image by the image sensor. The image processing module may apply the level of noise reduction to the first portion of the at least one image to generate an image of the scene. The image processing module may apply the level of reduced noise reduction to the second portion of the at least one image to generate an image of the iris for use in biometric identification.

In some embodiments, the image sensor can capture the view of the scene and the view of the iris as separable components within a single image. The image sensor may capture at least one image of the iris while illuminating the iris using infrared illumination. In certain embodiments, the image sensor can activate a plurality of sensor nodes of the image sensor. The first subset of sensor nodes can be adapted to capture an image of the iris primarily suitable for biometric identification. The second subset of sensor nodes may be adapted to capture primarily non-iris images.

In certain embodiments, the image processing module may apply noise reduction including an averaging or median function. The image processing module may apply noise reduction, including reducing both time varying and time invariant noise from the captured image. The image processing module may subtract noise from one image of the iris and noise from another image of the iris. In certain embodiments, the image processing module may reduce ambient noise in one image using ambient noise from another image. The image processing module may reduce ambient noise from one image captured in the presence of infrared illumination using ambient noise from another image captured without infrared illumination. The image processing module may perform gain or brightness control on the second portion of the at least one image to generate an image of the iris for use in biometric identification.

In another aspect, the present disclosure describes an apparatus for capturing images of an iris and a scene using a single image sensor. The device may include an image sensor and an image processing module. The image sensor may capture a view of the scene and a view of the iris within the at least one image. The image processing module may generate an image of the scene by applying a level of noise reduction to the first portion of the at least one image. The image processing module may apply the level of reduced noise reduction to the second portion of the at least one image to generate an image of the iris for use in biometric identification.

In some embodiments, the image sensor captures the view of the scene and the view of the iris as separable components within a single image. The image sensor may include, for example, a complementary metal oxide semiconductor (CMOS) sensor. The image sensor may comprise a plurality of sensor nodes, wherein the first subset of sensor nodes is adapted to capture an image of the iris primarily suitable for biometric identification, and the second subset of sensor nodes mainly capture a non-iris image. Can be adapted to In certain embodiments, the apparatus includes an illuminator for illuminating the iris using infrared illumination, and the image sensor captures at least one image of the illuminated iris.

In some embodiments, the noise reduction performed includes applying an average or median function to the captured image. Noise reduction can include reducing both time varying and time invariant noise from the captured image. In certain embodiments, the image processing module subtracts noise from one image of the iris with noise from another image of the iris. The image processing module may reduce ambient noise from one image captured in the presence of infrared illumination using ambient noise from another image captured without infrared illumination. In some embodiments, the image processing module may perform gain or brightness control on the second portion of the at least one image to generate an image of the iris for use in biometric identification.

Certain embodiments of the methods and systems disclosed herein can solve various challenges in obtaining high quality images of the iris as well as high quality images of the scene using a single sensor. For example, one challenge is probably unexpectedly related to the management of noise properties of the sensor. The inventors have found that the requirements for iris recognition and image quality of standard scenes are sometimes contradictory to noise. Since the pixel size of the imager becomes smaller and smaller, and thus the base noise level in each pixel increases or becomes more pronounced, noise may be very important. We determine that certain types of noise may actually be desirable or tolerated for iris recognition, relative to the quality of iris images obtained in standard picture acquisition modes, including noise reduction, for example. Thus, we may prefer to keep noise in the processed image during acquisition of the iris image, perhaps counterintuitively, in order to improve the performance of iris identification compared to images where conventional noise reduction has been performed.

Another challenge is related to the wavelength of illumination required for standard images and for iris images. Acquisition of an iris image typically uses infrared illumination, while standard images typically rely on visible illumination. These can be seen as conflicting constraints when integrated into a single system for the acquisition of both types of images. The present disclosure describes several approaches to solving this. For example and in one embodiment, different filters may be interleaved in front of the sensor. The filters may have different responses and visible responses to infrared light. RGB (red, green, blue) filters and filter patterns may be adapted for use in different embodiments. For example and in certain embodiments, systems and methods may interleave filters that pass infrared light and other filters that primarily pass color images. Examples of such approaches are in US Patent Publication 2007/0145273 and US Patent Publication 2007/0024931. Improvements to these approaches include the use of R, G, (G + I), B interleaved arrays, where I represents infrared light. This approach may have the advantage that the human visual system maintains or recovers the full resolution of the most sensitive G (green) signal. Another embodiment of the methods and systems solves this problem by using a removable or retractable IR-blocking filter that can be automatically or manually placed in front of the sensor during the standard image acquisition mode. In yet another embodiment, the systems and methods may overlay the IR-blocking filter only on the portion of the image sensor dedicated to iris recognition.

Embodiments of the systems and methods described herein may solve a third problem associated with the deformation of an image from ambient lighting. In some embodiments where infrared filtering or illumination is not optimal, images of the surrounding scene reflected from the cornea or eye surface may be observed during acquisition of the iris image. This can sometimes seriously affect the performance of iris recognition. Embodiments of the systems and methods described herein can acquire at least two images. One of the images may be captured with the controlled infrared illumination turned on, and at least a second image may be captured with the controlled infrared illumination turned off. The image processing module may process these at least two images to reduce or eliminate artifacts. For example, the image processing module may remove artifacts by subtracting the images from each other after aligning the images. Artifactual lighting or components are essentially unchanged between the two images, while the iris texture is illuminated by infrared light and exposed within one image, so that differences in the images can eliminate artifacts while maintaining the iris texture. Can be. The methods and systems can overcome nonlinearity in the sensor by identifying pixels that are at or near the nonlinear operating range of the sensor (eg, saturated or dark), and eliminate them from subsequent iris recognition processing, which This is because the image subtraction process in such areas may be nonlinear, and artifacts may still remain. In another embodiment of the methods, the deformation of the images can be managed by utilizing specific geometrical constraints of the location of the user, the device and the deformation source. In certain embodiments, the fact that a user can keep the device in front of his face during the iris acquisition mode can reduce or block the ambient light source from deforming in one sector of the acquired iris image. Methods and systems may, for example, limit iris recognition to this sector, avoiding issues related to image distortion.

The figures below show specific example embodiments of the methods and systems described herein, wherein like reference numerals designate like elements. Each illustrated embodiment illustrates, but not limiting, these methods and systems.
1A is a block diagram illustrating one embodiment of a networked environment with a client machine in communication with a server.
1B and 1C are block diagrams illustrating embodiments of computing machines for implementing the methods and systems described herein.
2 illustrates one embodiment of an image intensity profile corresponding to a portion of an image.
3A shows an image intensity profile of one embodiment for non-systematic noise.
3B shows an image intensity profile of one embodiment for systematic noise.
4 shows an image intensity profile of one embodiment for systematic noise.
5 shows an image intensity profile of one embodiment for sporadic noise.
6 illustrates one embodiment of an image intensity profile corresponding to a portion of an image where noise reduction has been performed.
7 is a diagram of one embodiment for an image of a view of a face that includes an iris texture.
8 illustrates one embodiment for an image intensity profile representing an iris texture.
9 illustrates one embodiment for an image intensity profile that represents the iris texture after noise reduction.
10 illustrates one embodiment for an image intensity profile representing iris texture and noise.
11 illustrates one embodiment for a system for obtaining scene images and iris images using a single sensor.
12 shows a chart showing the effect of noise on the acquired images.
FIG. 13 shows another embodiment for a system for obtaining a scene image and an iris image using a single sensor.
14 illustrates one embodiment for a system for obtaining a face image and an iris image using a single sensor.
15 shows a response profile based on a dual band pass filter.
16 illustrates one embodiment for the configuration of interleaved filters.
17 shows one embodiment for an image with artifacts reflected from the eye surface.
18 shows one embodiment for an image having iris textures and artifacts reflected from the eye surface.
19 illustrates another embodiment for a system for obtaining a face image and an iris image using a single sensor.
20 illustrates one embodiment for an image representing an iris texture with artifacts removed.
21 illustrates one scenario for the acquisition of face and iris images.
22 shows another embodiment for an image having iris textures and artifacts reflected from the eye surface.
FIG. 23 illustrates another embodiment for a system for obtaining a face image and an iris image using a single sensor.
24 illustrates another embodiment for a system for obtaining a face image and an iris image using a single sensor.
25 illustrates one embodiment for a system for obtaining a face image and an iris image using a single sensor and a mirror.
FIG. 26 illustrates one embodiment of a method for obtaining a face image and an iris image using a single sensor and a mirror.
27 illustrates the effect of ocular dominance on the acquisition of face images and iris images.
28 illustrates another embodiment for a system for obtaining a face image and an iris image using a single sensor and a mirror.
29 and 30 illustrate the effect of visual dominance on the acquisition of face images and iris images.
31 shows another embodiment for a system for obtaining a face image and an iris image using a single sensor and a mirror.
32 illustrates embodiments of sensor and mirror configurations.
33 illustrates another embodiment of a system for obtaining a face image and an iris image using a single sensor and a mirror.
34 illustrates another embodiment for a system for obtaining a face image and an iris image using a single sensor and a mirror.
35 illustrates another embodiment of a system for obtaining a face image and an iris image using a single sensor.
36 shows another embodiment for a system for obtaining a face image and an iris image using a single sensor.
37 illustrates another embodiment for a system for obtaining a face image and an iris image using a single sensor.
38 is a flow diagram illustrating one embodiment of a method for obtaining a scene image and an iris image using a single sensor.

Before discussing other aspects of systems and methods for acquiring scene images and iris images using a single sensor, a description of system components and features suitable for use in the present systems and methods is helpful. Can be. 1A illustrates one or more client machines 102A-102N (generally referred to herein as " clients ") in communication with one or more servers 106A-106N (generally referred to herein as " server (s) 106 "). One embodiment for a computing environment 101 that includes a machine (s) 102 ". A network is installed between the client machine (s) 102 and the server (s) 106.

In one embodiment, computing environment 101 may include an appliance installed between server (s) 106 and client machine (s) 102. The appliance can manage client / server connections, and in some cases, can load balance client connections among multiple back-end servers. The client machine (s) 102 may in some embodiments be referred to as a single client machine 102 or a single group of client machines 102, and the server (s) 106 may be a single server 106 or It may be referred to as a single group of servers 106. In one embodiment, a single client machine 102 communicates with two or more servers 106, and in another embodiment, a single server 106 communicates with two or more client machines 102. In yet another embodiment, the single client machine 102 communicates with a single server 106.

Client machine 102 may, in some embodiments, be client machine (s) 102; Client (s); Client computer (s); Client device (s); Client computing device (s); Local machine; Remote machine; Client node (s); Endpoint (s); Endpoint node (s); Or any second machine. Server 106 may, in some embodiments, include server (s); Local machine; Remote machine; Server farm (s); Host computing device (s); Or one of the first machine (s).

The client machine 102 may, in some embodiments, be software; program; Executable instructions; Virtual machine; Hypervisor; A web browser; Web-based client; Client-server applications; Thin-client computing clients; ActiveX control; Java applet; Software related to voice over internet protocol (VoIP) communications, such as soft IP phones; An application for streaming video and / or audio; An application for facilitating real time data communication; HTTP client; FTP client; Oscar client; Telnet client; Or execute, run, or provide an application, which may be any of any other set of executable instructions. Still other embodiments include a client device 102 that displays application output generated by an application running remotely on a server 106 or other remotely deployed machine. In such embodiments, client device 102 may display the application output in an application window, browser, or other output window. In one embodiment, the application is a desktop, while in other embodiments, the application is an application that creates a desktop.

Computing environment 101 includes two or more servers 106A- 106N so that servers 106A- 106N can be grouped together in server farm 106 together. Server farm 106 includes servers 106 that are geographically distributed and logically grouped together in server farm 106, or servers 106 that are placed in close proximity to one another and logically grouped together in server farm 106. can do. Geographically dispersed servers 106A- 106N within server farm 106 may communicate using WAN, MAN, or LAN in some embodiments, with different geographic areas being different continents; Different regions of the continent; Different countries; Different states; Different cities; Different campuses; Different rooms; Or as any combination of the geographic locations described above. In some embodiments, server farm 106 may be managed as a single entity, while in other embodiments, server farm 106 may include multiple server farms 106.

In some embodiments, server farm 106 is a server running a substantially similar type of operating system platform (e.g., WINDOWS NT, UNIX, LINUX, or SNOW LEOPARD manufactured by Microsoft, Washington, Redmond). 106). In other embodiments, server farm 106 may include a first group of servers 106 running a first type of operating system platform, and a server farm 106 running on a second type of operating system platform. It can include two groups. Server farm 106 may include servers 106 running different types of operating system platforms in other embodiments.

Server 106 may be any server type in some embodiments. In other embodiments, server 106 may comprise the following server types: file server; Application server; A web server; Proxy server; Appliances; Network appliances, gateways; An application gateway; Gateway server; Virtualization server; Deployment server; SSL VPN server; firewall; A web server; An application server or master application server; A server 106 executing an active directory; Or any server 106 running an application acceleration program that provides firewall functionality, application functionality or load balancing functionality. In some embodiments, server 106 may be a RADIUS server that includes a remote authentication dial-in user service. Some embodiments receive requests from the client machine 102, send the request to the second server 106B, and respond with a response from the second server 106B to the request generated by the client machine 102. A first server 106A. The first server 106A can obtain a list of applications available to the client machine 102 as well as address information associated with the application server 106 hosting the application identified in the list of applications. In addition, the first server 106A may use the web interface to provide a response to the client's request and communicate directly with the client 102 to provide the client 102 with access to the identified application. have.

Client machines 102 may, in some embodiments, be a client node seeking access to the resources provided by server 106. In other embodiments, server 106 may provide access to hosted resources to clients 102 or client nodes. Server 106 acts as a master node in some embodiments, in communication with one or more clients 102 or server 106. In some embodiments, the master node may identify and provide address information related to the server 106 hosting the requested application to one or more clients 102 or server 106. In yet other embodiments, the master node may be a server farm 106, a client 102, a cluster of client nodes 102, or an appliance.

One or more clients 102 and / or one or more servers 106 may transmit data over a network 104 installed between machines and appliances in computing environment 101. The network 104 may include one or more subnetwork and may be installed between any combination of clients 102, servers 106, computing machines and appliances included within the computing environment 101. . In some embodiments, network 104 includes a local area network (LAN); Urban area network (MAN); Wide area network (WAN); Main network 104 including a plurality of subnetworks 104 located between client machines 102 and servers 106; Main public network 104 having a private subnetwork 104; Main private network 104 having a public subnet 104; Or primary private network 104 with private subnetwork 104. Still other embodiments include the following network types: point-to-point network; Broadcast network; Telecommunication networks; Data communication network; Computer network; Asynchronous Transfer Mode (ATM) network; Synchronous Optical Network (SONET) network; Synchronous Digital Hierarchy (SDH) network; Wireless network; Wired network; Or network 104, which may be of any type of networks 104 including a wireless link, which may be an infrared channel or satellite band. The network topology of the network 104 may differ in different embodiments, and possible network topologies include a bus network topology; Star network topology; Ring network topology; Repeater-based network topology; Or tiered-star network topology. Additional embodiments may include a network 104 of mobile telephone networks that use a protocol to communicate between mobile devices, the protocol comprising AMPS; TDMA; CDMA; GSM; GPRS; UMTS; 3G; 4G; Or any other protocol capable of transferring data between mobile devices.

1B is shown an embodiment of the computing device 100, and the client machine 102 and server 106 shown in FIG. 1A may be any of the computing device 100 shown and described herein. As an embodiment it may be deployed and / or executed. Within computing device 100, the following components: central processing unit 121; Main memory 122; Storage memory 128; Input / output (I / O) controller 123; Display devices 124A-124N; Installation device 116; And a system bus 150 in communication with the network interface 118. In one embodiment, storage memory 128 includes an operating system, software routines, and client agent 120. I / O controller 123 is further connected to keyboard 126 and pointing device 127 in some embodiments. Other embodiments may include an I / O controller 123 connected to two or more input / output devices 130A-130N.

1C illustrates one embodiment for computing device 100, wherein client machine 102 and server 106 shown in FIG. 1A are any implementation of computing device 100 shown and described herein. As an example it may be deployed and / or executed. Computing device 100 includes a system bus 150 in communication with the following components: bridge 170 and first I / O device 130A. In another embodiment, the bridge 170 is further in communication with the main central processing unit 121, wherein the central processing unit 121 is the second I / O device 130B, the main memory 122, and the cache memory ( 140 may be further communicated with. The central processing unit 121 includes I / O ports, a memory port 103, and a main processor.

Embodiments of the computing machine 100 include the following component configurations: logic circuits that process these in response to instructions fetched from the main memory unit 122; Those produced by Intel Corporation; Those produced by Motorola; Microprocessor units such as those manufactured by Transmeta of Santa Clara, California; RS / 6000 processors, such as those manufactured by IBM; Processors such as those manufactured by Advanced Micro Devices; Or a central processing unit 121 characterized by any other combination of logic circuits. Still other embodiments of the central processing unit 122 include a microprocessor, a microcontroller, a central processing unit with a single processing core, a central processing unit with two processing cores, or a central processing unit with two or more processing cores. And any combination of

1C shows a computing device 100 that includes a single central processing unit 121, in some embodiments, the computing device 100 may include one or more processing units 121. In such embodiments, computing device 100 may store and execute firmware or other executable instructions, which, when executed, instruct one or more processing units 121 to execute instructions concurrently or instructions for a single piece of data. Instructs them to run at the same time. In other embodiments, computing device 100 may store and execute firmware or other executable instructions that, when executed, instruct one or more processing units to each execute a section of a group of instructions. For example, each processing unit 121 may be instructed to execute a portion of a program or a specific module within the program.

In some embodiments, processing unit 121 may include one or more processing cores. For example, the processing unit 121 may have two cores, four cores, eight cores, or the like. In one embodiment, the processing unit 121 may include one or more parallel processing cores. The processing cores of the processing unit 121 may access the available memory as a global address space in some embodiments, or in other embodiments the memory within the computing device 100 may be segmented, and within the processing unit 121. Can be assigned to a specific core. In one embodiment, one or more processing cores or processors in computing device 100 may each access local memory. In another embodiment, memory in computing device 100 may be shared between one or more processors or processing cores, while other memory may be accessed by specific processors or subsets of processors. In embodiments where computing device 100 includes two or more processing units, multiple processing units may be included within a single integrated circuit (IC). Such multiple processors may be linked together by an internal high speed bus, which in some embodiments may be referred to as an element interconnect bus.

In embodiments where computing device 100 includes one or more processing units 121, or processing units 121 that include one or more processing cores, processors may execute a single instruction simultaneously on multiple pieces of data. Or (SIMD), or in other embodiments, can execute multiple instructions simultaneously on multiple pieces of data (MIMD). In some embodiments, computing device 100 may include any number of SIMD and MIMD processors.

Computing device 100 may include an image processor, graphics processor or graphics processing unit in some embodiments. The graphics processing unit may comprise any combination of software and hardware, and may further enter graphics data and graphics instructions, render graphics from the input data and instructions, and output the rendered graphics. In some embodiments, the graphics processing unit may be included within the processing unit 121. In other embodiments, computing device 100 may include one or more processing units 121, wherein at least one processing unit 121 is dedicated to processing and rendering graphics.

One embodiment of the computing machine 100 includes a central processing unit 121 that communicates with the cache memory 140 via an auxiliary bus, also known as a backside bus, while another implementation of the computing machine 100. An example includes a central processing unit 121 in communication with a cache memory via a system bus 150. The local system bus 150 may be used by the central processing unit in some embodiments to communicate with two or more types of I / O devices 130A- 130N. In some embodiments, local system bus 150 may include a bus of the following type: a VESA VL bus; ISA bus; An EISA bus; A micro channel architecture (MCA) bus; PCI bus; PCI-X bus; PCI-express bus; Or NuBus. Other embodiments of computing machine 100 include I / O devices 130A-130N, which are video displays 124 in communication with central processing unit 121. Still other versions of computing machine 100 include a processor 121 that is connected to I / O devices 130A- 130N via one of the following connections: HyperTransport, High Speed I / O, or InfiniBand. Further embodiments of the computing machine 100 may communicate with one I / O bus 130A using a local interconnect bus and the processor 121 using a direct connection to communicate with a second I / O bus 130B. It includes.

Computing device 100 includes main memory unit 122 and cache memory 140 in some embodiments. Cache memory 140 may be any memory type, and in some embodiments, may include the following memory types: SRAM; BSRAM; Or EDRAM. Other embodiments include the following memory types: static random access memory (SRAM), burst SRAM or SynchBurst SRAM (BSRAM); Dynamic random access memory (DRAM); High speed page mode DRAM (FPM DRAM); Advanced DRAM (EDRAM), Extended Data Output RAM (EDO RAM); Extended data output DRAM (EDO DRAM); Burst extended data output DRAM (BEDO DRAM); Advanced DRAM (EDRAM); Synchronous DRAM (SDRAM); JEDEC SRAM; PC100 SDRAM; Double data rate SDRAM (DDR SDRAM); Advanced SDRAM (ESDRAM); SyncLink DRAM (SLDRAM); Direct Rambus DRAM (DRDRAM); Ferroelectric RAM (FRAM); Or a cache memory 140 and a main memory unit 122, which may be any of any other memory type. Further embodiments include a system bus 150; Memory port 103; Or a central processing unit 121 that can access the main memory 122 via any other connection, bus or port that allows the processor 121 to access the memory 122.

One embodiment of the computing device 100 is the following installation devices 116: CD-ROM drive, CD-R / RW drive, DVD-ROM drive, tape drives in various formats, USB device, bootable Support for media, bootable CDs, bootable CDs for GNU / Linux distributions such as KNOPPIX®, hard drives or any other device suitable for installing applications or software. The applications may include the client agent 120 or any portion of the client agent 120 in some embodiments. The computing device 100 may further include a storage device 128, which may be any one or more hard disk drives or one or more redundant arrays of independent disks, the storage device comprising an operating system, software, programs, applications. , Or at least a portion of the client agent 120. Additional embodiments of computing device 100 include installation device 116 used as storage device 128.

Computing device 100 may be a standard telephone line, LAN or WAN link (e.g., 802.11, T1, T3, 56kb, X.25, SNA, DECNET), broadband connection (e.g., ISDN, frame relay, ATM, Gigabit). Network interface 118 for interfacing to a LAN, WAN, or Internet via various connections, including but not limited to Ethernet, Ethernet-over-SONET), wireless connections, or some combination of any or all of the foregoing. It may further include. Connections also include various communication protocols (eg TCP / IP, IPX, SPX, NetBIOS, Ethernet, ARCNET, SONET, SDH, Fiber Distributed Data Interface (FDDI), RS232, RS485, IEEE 802.11, IEEE 802.11a, IEEE 802.11b). , IEEE 802.11g, CDMA, GSM, WiMax, and direct asynchronous access). One version of computing device 100 may be any type and / or type of gateway, such as Secure Sockets Layer (SSL) or Transport Layer Security (TLS), or the Citrix Gateway Protocol manufactured by Citrix Systems. Or a network interface 118 that can communicate with additional computing devices 100 'via a tunneling protocol. Versions of the network interface 118 include: an embedded network adapter; Network interface card; PCMCIA network card; Card bus network adapters; Wireless network adapters; USB network adapter; modem; Or any other device suitable for interfacing the computing device 100 to a network capable of communicating and performing in the methods and systems described herein.

Embodiments of computing device 100 include the following I / O devices 130A-130N: keyboard 126; Pointing device 127; mouse; Track pads; Optical pens; Trackball; MIC; Drawing tablet; Video display; speaker; Inkjet printers; Laser printers; And dye-sublimation printers; Or any other input / output device capable of performing the methods and systems described herein. I / O controller 123 may, in some embodiments, connect to multiple I / O devices 130A-130N to control one or more I / O devices. Some embodiments of I / O devices 130A-130N may be configured to provide a storage or installation medium 116, while other embodiments may include a USB flash drive line of devices manufactured by TwinTech Industries, Inc. It can provide a universal serial bus (USB) interface to accommodate the same USB storage devices. Still other embodiments include a system bus 150 and an external communication bus, such as a USB bus; Apple Desktop Bus; RS-232 serial connection; SCSI bus; FireWire bus; FireWire 800 bus; An Ethernet bus; AppleTalk bus; Gigabit Ethernet Bus; Asynchronous transmission mode bus; HIPPI bus; Super HIPPI bus; SerialPlus bus; SCI / LAMP bus; FibreChannel bus; Or I / O device 130, which may be a bridge between serially attached small computer system interface buses.

In some embodiments, computing machine 100 may run any operating system, while in other embodiments, computing machine 100 may include the following operating systems: versions of Microsoft Windows operating systems; Different releases of Unix and Linux operating systems; Any version of the MAC OS produced by Apple Computer; OS / 2 manufactured by IBM Corporation; Google's Android; Any embedded operating system; Any real time operating system; Any open source operating system; Any proprietary operating system; Any operating systems for mobile computing devices; Or any other operating system. In yet another embodiment, computing machine 100 may execute multiple operating systems. For example, the computing machine 100 may run PARALLELS or another virtualization platform capable of running or managing a virtual machine running a first operating system, and the computing machine 100 may be configured differently from the first operating system. 2 Run the operating system.

Computing machine 100 includes the following computing devices: a computing workstation; Desktop computer; A laptop or notebook computer; server; Handheld computers; Mobile phone; Portable telecommunication devices; A media playback device; Game system; Mobile computing devices; Netbooks, tablets; Devices of the IPOD or IPAD family of devices manufactured by Apple Computer; Any one of the PLAYSTATION family of devices manufactured by Sony; Any one of the Nintendo family of devices made by Nintendo; Any one of the XBOX family of devices manufactured by Microsoft; Or be capable of communicating and being implemented within any other type and / or form of computing, telecommunications, or media device having sufficient processor power and memory capacity to perform the methods and systems described herein. have. In other embodiments, computing machine 100 may include the following mobile devices: a JAVA-enabled cellular telephone or a personal digital assistant (PDA); Any computing device having different processors, operating systems, and input devices compatible with the device; Or a mobile device such as any of the other mobile computing devices capable of performing the methods and systems described herein. In still other embodiments, computing device 100 may include the following mobile computing devices: at least one series of BlackBerry or other handheld devices manufactured by Research In Motion Limited; An iPhone made by Apple Computer; Palm Pre; Pocket PC; Pocket pc phone; Android phone; Or any other handheld mobile device. Specific system components and features that may be suitable for use in the present systems and methods have been described, and additional aspects are discussed below.

2 illustrates an example image of a typical scene or object (eg, a house) obtained by a conventional image sensor. The image sensor may include, but is not limited to, for example, a charge-coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) active pixel sensor. The graph or intensity profile corresponding to the image represents the intensity value I of the pixels on the vertical axis and the corresponding spatial position X, relative to the cross-sectional area indicated by line P2. The light and dark points in the intensity profile correspond to the light and dark points in the image as shown. Typically, there may be substantial noise in the signal, represented by variations in intensity, even within uniformly illuminated areas (eg, areas corresponding to the door of the house). Noise can be derived from several sources, such as amplifier noise and shot-noise, anisotropic (systematic) noise, and sporadic noise. Shot noise is related to quantum effects in which a finite number of photons are collected within a particular pixel-well within a finite period of time. The smaller the pixel size, the more shot noise may occur. This is because there may be fewer photons to infer the measurement of the incident illumination. As pixel dimensions become smaller, the focal length of the relevant optics for a given image resolution may also decrease linearly. This can reduce the thickness of the lens / sensor component combination. However, as requirements for sensor resolution increase, and as space constraints on sensors and their associated optics become more stringent, sensor and image pixel sizes decrease appropriately to accommodate the requirements and constraints. Should be. The result of the reduction in pixel size is a substantial increase in the noise of the sensor. This type of noise, as well as amplifier noise, can be characterized as time varying and unsystematic as shown in FIG. 3A.

Another type of noise is anisotropic or systematic / periodic noise. Periodic noise can be caused, for example, by differences in amplifier gains in the read path of the image sensor. For example, different rows and columns may pass through different amplifiers with slightly different gains. This type of systematic noise is shown in FIG. 3B, where the intensity profile, which must be uniformly flat, is in fact periodically fluctuating in one dimension (eg across the image). 4 shows an example of sporadic noise introduced into an image, which may be evident over multiple images. For example, occasional pixels in an array of sensor nodes may have degraded sensitivity, or may not function, or have limited or excessive gain, such that the pixels may be brighter or darker as shown. .

Problems resulting from noise are typically solved by performing noise reduction in image processing module 220. Image processing module 220 may use any type of spatial central filtering or region-selective averaging as shown in FIG. 5. There are many ways to perform noise reduction and only identify central filtering and region-selective averaging for illustrative purposes. 6 shows an intensity profile that may arise from noise reduction. Although noise reduction may have essentially removed the noise, the image processing module 220 retained features (eg, bright and dark points) corresponding to real objects and edges in the scene. From the user's point of view, image quality is typically unacceptable (eg, noisy) in FIG. 1, while in FIG. 6 the quality is considered to be better.

7 shows an image of iris I1 and face F1. The image may be obtained using an optimal iris image acquisition system, for example according to the specifications described in the National Institute of Standards and Technology (NIST) standards. Such specifications may include those described in ANSI / INCITS 379-2004, Iris Image Interchange Format. Referring to FIG. 7, the texture of the iris is represented by lines in the circular region indicated by I1. 8 shows one representation of the intensity profile of the texture of the iris. In some embodiments, the similarity between FIG. 8 (intensity profile of the iris texture pattern) and FIG. 2 (intensity profile of the noise signal) may be quite apparent. The reason for such similarity is that the source of each signal / pattern is characterized by a random process. In the case of the iris, the paper tear is signaled by the tearing of the iris tissue before birth, very similar to a different process each time it occurs. In the case of sensor noise, shot noise and other noises are generated by random time-varying physical processes.

The frequency characteristics of the iris signal “texture” have been characterized to some extent in the NIST standards [ANSI / INCITS 379-2004, Iris Image Interchange Format], for example millimeters (mm) for different iris diameter ranges. Minimum resolution values corresponding to lines / pairs may be specified. The iris diameter may depend on the particular optical configuration. For example, for an iris diameter between 100-149 pixels, the defined pixel resolution can be at least 8.3 pixels per millimeter, with optical resolution at 60% modulation of at least 2.0 line-pairs per millimeter. For an iris diameter between 150-199 pixels, the defined pixel resolution can be at least 12.5 pixels per millimeter, with optical resolution at 60% modulation of at least 3.0 line-pairs per millimeter. For an iris diameter with 200 or more pixels, the defined pixel resolution can be at least 16.7 pixels per millimeter, with optical resolution at 60% modulation of at least 4.0 line-pairs per millimeter. In certain embodiments, for other diameters, defined pixel resolution and / or optical resolution combinations may be suitable.

9 shows the intensity profile of the iris texture after some of the noise reduction processing described above has been performed. In this example case, the iris texture is essentially removed by noise reduction. This is because noise reduction algorithms such as region-specific averaging may not distinguish between iris texture and noise. Thus, in most image capturing devices, the standard or conventional noise reduction may be a limitation when adapted to perform iris recognition.

The present systems and methods can solve this problem by recognizing certain characteristics related to iris recognition. FIG. 10 illustrates, in one embodiment, with the intensity profile of the sensor noise indicated by a dotted line (eg, in NIST standards [ANSI / INCITS 379-2004, Iris Image Interchange Format]). As such, the intensity profile of the optimally obtained iris texture is shown. Certain iris recognition processes involve identifying a lack of statistical independence between the matched signal and the probe signal. One significance may be that a match is typically declared by a comparison yielding results that are not likely to be achieved by a random process. Thus, adding significant random and time varying noise to the original iris signal may not 1) increase the false match rate significantly because false matches arise from non-random matching, and 2) There may be a limited impact on the false rejection rate for an individual if the texture is in general or in essence exceeding the texture of the sensor noise (eg, the images themselves appear noisy to the observer), 3 ) If the texture of the iris signal has a similar or smaller scale than the scale of the sensor noise, it can increase the false rejection rate for the user (along with other limited results).

However, adding systematic noise to the original iris signal, for example, as shown in FIG. 3, can trigger a false match because the comparison between the two data sets was not achieved by a random process. This can be calculated. Thus, certain embodiments of the methods and systems provide noise (eg, in the captured iris image) to improve the performance of iris identification compared to images with reduced noise levels (eg, through noise reduction). May even prefer the presence (eg, counterintuitive) of even a significant level of noise. In some embodiments, the present systems can reduce or eliminate the level of unsystematic noise reduction applied to an image when the image is intended for iris recognition. The resulting images may seem extremely noisy to the observer compared to the processed image (eg, with noise reduction applied). However, the performance of iris recognition can be greatly improved if noisy images are used instead for iris recognition. In some specific hardware implementations, noise reduction algorithms may be enabled, hard-coded, and not turned off. Some embodiments of the present methods and systems allow control over noise reduction algorithms to avoid noise reduction in the frequency bands expected for the iris texture as described elsewhere herein.

11 illustrates an example implementation of an approach in which the main processor may control an image signal processor, eg, a low level image signal processor. In the mode in which iris recognition is performed, a signal may be sent to the image signal processor to alter the noise reduction process as described above. Depending on the magnitude of the systematic noise, such noise can be removed (e.g., using dynamic low correction, which is covered by pixels at the edge of the sensor and can be used for sensor calibration), or the magnitude of the noise can be reduced to an iris texture. It can be left as is if it is significantly smaller than the signal magnitude of. For example, FIG. 12 shows a table summarizing a number of scenarios and describes how different types of noise can affect the performance of iris recognition and / or the quality of the visible image in different image acquisition modes.

Another challenge associated with obtaining an optimal standard scene image and iris image on the same sensor is related to the wavelength of illumination required for the standard image and the iris image. Iris images typically require infrared illumination, while standard images typically require visible illumination. Sometimes contradictory restrictions exist. Some embodiments of the present systems can be configured to solve this by interleaving filters with different responses to infrared and visible light. Such systems may use one of a plurality of different configurations of such filters for the image sensor when capturing an image. One example of a filter that can be integrated or altered to produce an interleaved filter is a filter with a Bayer RGB (red, green, blue) filter pattern (see, eg, US Pat. No. 3,971,065). Filters that pass (mainly, only or only) infrared light can be interleaved with other filters that pass (mainly, only or only) color or visible light. Some embodiments of filters providing selected filtering are described in US Patent Publication 20070145273 and US Patent Publication 20070024931. Some embodiments of the present systems and methods use R, G, (G + I), B interleaved arrays instead. Some of these systems have the ability of a human visual system to maintain the full (or substantially complete) resolution of the most sensitive G (green) signal.

In iris recognition mode, the magnitude of the G (green) response is typically much smaller than the magnitude of the infrared response due to incident infrared illumination. In some embodiments, the estimate of the infrared signal response I in the iris recognition mode can be recovered by subtracting the (G) signal from the adjacent (G + I) signal. In the standard image acquisition mode, the R, G, (G + I), and B signals can be processed to recover the estimate G 'of the G in the pixel where G + I is recovered. For example, when R, G, T, B pixel arrays are used, where T is completely transparent, various methods may be used to generate such estimates. The T pixel in such an implementation may include signals of R, G, B and I signals accumulated or superimposed together. This can be a problem. If the T pixel filter is truly transparent, for effective performance, the sum of the R, G, B, and I responses must still be within the dynamic range of the pixel. For a given integration time and pixel region throughout the entire image, this means that the saturation of the T pixels (R + G + B + I) may occur so that the dynamic range of R, G, and B pixels is not fully available. do. It may be possible to set different pixel areas and gain for a T pixel relative to other R, G, B pixels, but it may be expensive to implement. One improvement that can be incorporated into the present systems is the use of neutral density filters instead of transparent filters. Neutral density filter can reduce the scale of illumination of all wavelengths (R, G, B, I) at that pixel, allowing sufficient or extensive pixel capacities to be used at R, G, B pixels Noise can be reduced. As an example a neutral density filter having a value of 0.5 to 0.6 can be selected. Typically the green signal can contribute about 60% of the luminance signal comprising R, G and B coupled together.

If the T filter is truly transparent, the overall dynamic range of the sensor typically needs to be reduced to accommodate the range of T pixels and keep it within the linear range, at the expense of the signal-to-noise ratio of the R, G, and B pixels. will be. In some embodiments of the present systems, by integrating the R, G, G + I, B filter arrays and because the red and blue signals are not present in the G + I pixel, the overall dynamic range of the sensor is R, G, T, B This can be increased relative to the dynamic range of the array, thus increasing the signal-to-noise ratio.

Regarding the wavelength of illumination, another approach incorporated into some embodiments of the present methods and systems for obtaining an optimal standard scene image and iris image on the same sensor is to multiplex an infrared cut filter over a standard image sensor or lens. Or deploying. In one embodiment, as shown, for example, in FIG. 14, some of the sensors (eg, 20% of the sensors or sensor nodes) may be designated primarily for iris recognition, while others (eg, , 80%) can be used for standard image acquisition. As in this example, the lower portion of the sensor (eg 80%) may be covered by a standard IR cut filter. The remaining 20% of the sensor can remain uncovered. In the iris recognition mode, the covered area can be ignored. For example, an iris recognition application running on an image capturing device can guide a user to place his eye within a sensing area of an uncovered 20% area. Feedback mechanisms can guide the user to move the image capturing device to position the user's iris within the appropriate capture area. For example, since the face will be visible in the remaining 80% of the imager, this may optionally be used for user guide feedback, with icons appearing instead of the eye area. In some embodiments, the image sensor can adjust its orientation to capture an image of the user's iris using the uncovered area.

Another approach incorporated within some embodiments of the present systems and methods uses a dual band pass filter over all or a substantial portion of a color imager or sensor. Such a filter can pass both R, G, B signals and infrared signals in selected bands, such as bands near 850 nm or 940 nm, and can yield a frequency response as shown in FIG. 15. . In another embodiment, the image acquisition system may use an IR blocking filter that can be automatically or manually placed or slid into place over at least a portion of the image sensor when the device is in a standard image capture mode. For example, the IR blocking filter may cover a portion of the image sensor so that it is aligned with the user's eyes to capture the iris image. Other portions of the image sensor may capture portions of the user's face, for example. The IR cutoff filter may be placed at one end of the sensor, so that the sensor and thus the captured image are in three or more regions (eg, non-IR-blocking, IR-blocking and non-IR-blocking). Rather, it can have two distinct regions (IR-blocking and non-IR-blocking). This allows a larger and more continuous non-iris part of the scene (eg face) to be obtained, which in turn can be used for face identification for example. In some embodiments, a visible light filter or an IR pass filter may be disposed above the image sensor when the device is in an iris image capture mode (eg, as an option).

In some embodiments, the image acquisition system may interleave infrared cutoff and infrared pass filters across the sensor as shown, for example, in FIG. 16. Interleaved filters may be configured in a variety of other ways, such as checker box arrangement, stripes of varying widths or the use of other alternating and / or repeatable patterns. In iris recognition mode, the response from sensor pixels / nodes below the IR pass filter bands is used for iris recognition, while the response from sensor pixels / nodes below the IR blocking filter bands is in standard image acquisition mode. Used in In some embodiments, both standard and iris images can be obtained using a single image capture, such as by separating the IR and non-IR image components corresponding to an interleaving pattern.

In some embodiments, the image obtained by the image sensor can be affected or deformed by ambient light. For example, in some embodiments, if infrared filtering and / or illumination is not optimal, images of the scene may be reflected from the surface of the eye (eg, the cornea) during acquisition of the iris image. An example of this is shown in FIG. The reflection of the image (eg, on the cornea of the eye) may be, for example, a reflection of a scene including houses around the user. Such reflections may be referred to as artifacts. Above, we explain how systematic noise can adversely affect the performance of iris recognition. The artifacts are similar methods: at least two images, one image with the controlled infrared illumination turned on as shown in FIG. 18 and a controlled infrared illumination turned off as shown in FIG. 17. It can be overcome using a method of obtaining at least a second image in. The image processing module may process these at least two images to reduce or eliminate artifacts. For example, in some embodiments, the image processing module may subtract images from each other after aligning the images as shown in the processing diagram of FIG. 19. Artifact generation illumination is essentially unchanged between the two images, while the iris texture is illuminated by infrared illumination, so that artifacts can be eliminated by making a difference, while the iris texture is maintained. The remaining iris texture is shown by the lines in the iris in FIG. 20. The system can further overcome the nonlinearity of the sensor by, for example, identifying pixels that are in or near (eg, saturated or dark) the nonlinear operating range of the sensor. The image processing module may exclude the identified pixels from subsequent iris recognition processing. Image subtraction processing in such areas can be nonlinear, so artifacts can be maintained using a subtraction approach.

Another embodiment of the methods manages the deformation of the images by using specific geometrical constraints on the location of the user, the image capturing device, and the source of the deformation or artifacts. The image processing module causes the image capturing device to deform within a sector of the acquired iris image, for example, as shown in FIG. 21 when the user holds the image capturing device in front of the user's face during iris recognition mode. It may be configured to recognize that it can reduce or even block the sources of ambient light. As shown in FIG. 22, the image processing module can limit iris recognition mainly or only to this sector, thereby avoiding issues related to image deformation. In some embodiments, iris recognition based on this sector of the image may be weighted higher than other sectors in determining the biometric match.

In some embodiments, infrared illumination is not readily available or warranted during image capture. Image acquisition system 200 may be configured to control and / or provide infrared illumination. The image acquisition system may reduce power usage by illuminating with an infrared source (eg, LEDs) when the device is in iris recognition mode as shown in FIG. 23.

FIG. 24 illustrates one embodiment of an image acquisition system 200 utilizing some features of the systems and methods described herein. Image acquisition system 200 may be implemented within a device, such as a mobile and / or handheld device. The device may include a screen with a sensor. Infrared LEDs can provide illumination. The user can switch between the iris recognition mode and the standard picture capture mode using a touch screen or other input device (eg, keyboard, button or voice command recognition). The device may include an application, through which the user may activate the image capturing mode. The application may further find the user's iris or provide further feedback or guidance mechanisms to guide the user to move the user's iris into the appropriate capture area. In some embodiments, an optional IR blocking filter may be manually or automatically activated or moved over the image sensor when in iris image capture mode. Other filters (eg, IR pass filter) may be integrated and / or activated in the appropriate mode (s). In certain embodiments, certain features of image acquisition system 200 may be included in an add-on accessory or sleeve for a mobile or existing device. As an example, such features may include an infrared illuminator, one or more filters, and / or an interface (eg, wireless or physical) to a mobile or existing device.

In some embodiments, image acquisition system 200 may include infrared illuminators embedded within the screen of image acquisition system 200 for illumination of a user's eye using infrared illumination. Screens and displays typically use white LED illumination below the LCD matrix. By replacing or adding some of the visible light LEDs to near infrared illuminators, the source of IR illumination can be provided by the display itself. In such embodiments, the image acquisition system 200 may not require additional fixtures or areas on the image acquisition system 200 to provide infrared illumination, thus saving space.

In certain embodiments, image acquisition system 200 may include, for example, a visible illuminator having two illumination intensities. The visible illuminator can be turned on at low power during the iris image acquisition mode. Low power lighting can be selected to not distract or distract the user. In some embodiments, the luminance level in the low power mode can be at least twice as dark as the maximum luminance of the visible illuminator. The latter luminance level can be used, for example, to illuminate a wider scene. Low power visible illuminators can be used to constrict the iris and increase the iris area whether the user is in the dark or not. However, since visible illuminators can be close to the eye, some of the filters described above can still deliver significant visible light into the sensor. Thus, in some embodiments, visible light is turned off before images of the iris are acquired while the near infrared illuminator is turned on. In an alternative embodiment, the screen itself may be used as the source of visible light.

In some embodiments, one advantage of using a single sensor in image acquisition system 200 is that the space occupied by the system can be minimized compared to the use of dual sensors. In either case, however, an important consideration is the ability of the user and / or operator to effectively use a single sensor or dual sensor device.

In some embodiments, a mirrored surface may be used to help guide the user to align the user's iris with the proper capture area of the image sensor. The mirrored surface can feed back the user's location to the user as shown in FIG. 25, in which case the user holds the device in front of him, and a virtual image of some of the user's face is the distance from the user to the device. It is observed at twice. However, due to the nature of the human vision system, the ocular dominance and the requirements of the iris recognition system, the optimal size of the mirror may not be scaled linearly with the user's distance to the mirror as can be expected. . Indeed, under some conditions, increasing the size of the mirror to try and improve iris recognition performance can degrade performance or cause difficulty in alignment.

Visual dominance tends to favor visual input from one eye or the other. This occurs in most people, with two thirds of them having right eye dominance and one third of people having left eye dominance. In order to maximize the size of the iris image to be recovered while minimizing the size of the mirror used to guide the user, the present systems and methods address visual dominance and combine the attributes of visual dominance with the constraints of iris recognition.

FIG. 26 shows a reflective field of view of a mirror sized to allow both eyes to comfortably occupy the field of view. In some embodiments, the width of the mirror allows the reflected field of view at the viewing distance of the image acquisition device 200 to be at least about 50% wider than the reflection of the eye gap. For illustration purposes, the user is shown in the center of the mirror. However, FIG. 27 actually shows that due to the visual dominance the user is typically located on one side of the mirror so that his dominant vision is closer to the center of the mirror. If the width of the mirror's field of view is greater than 50% of the field of view of the user's normal eye interval (6.5-7 cm), the eyes may remain within the field of view. Thus, both eyes may be acquired by the image acquisition system 200 for people with visual dominance, since both eyes may remain in the field of view of the image sensor in that case. However, the iris diameter in the captured image can be relatively small because the lens of the sensor is typically chosen to cover a wide field of view.

28 shows a configuration for acquiring images of both eyes using a smaller mirror without considering visual dominance. The field of view of the mirror is smaller, thus minimizing its area on any image acquisition system 200. Both eyes can be captured when the user is placed in the center of the mirror. However, as mentioned above, due to visual dominance, the user is typically placed on the right or left side of this optimal position as shown in FIGS. 29 and 30. In such a scenario, one of the eyes may be located outside the field of view of the camera. Thus, this configuration has a reasonably large mirror, but even if the lens can be configured to capture both eyes (when in the center position), due to the visual dominance, the image acquisition system 200 will in fact reliably only have one eye. Can be captured.

FIG. 31 shows a design using a smaller mirror so that a higher resolution iris image is obtained (ie, improving iris recognition performance) compared to FIG. 30, but only dominant vision is observed by the user. By limiting the size of the mirror so that only the dominant vision is within the field of view, the user's visual system tends to select the left or right eye with a variable or unpredictable response (e.g., shifted left or right) within the field of view. In contrast to eyes), it is forced to be a binary response (eg, left or right eye). In some embodiments, image acquisition system 200 may operate or include a mirror having a diameter of about 14 mm at an operating distance of about 9 "such that the reflective field of view of the mirror is approximately two conventional iris diameters (2). x 10.5 mm) Figure 32 summarizes and shows the size of the effective field of view of the mirror and its relationship to one or two-eye capture and also the size of the obtained iris image.

33 illustrates one embodiment of an image acquisition system 200 with an IR cut filter disposed over a portion of the sensor. A face or other image can be obtained by part of the sensor, and the image for iris recognition is obtained by the part covered by the IR blocking filter. The visual dominance tends to provide uncertainty in the horizontal direction due to the horizontal configuration of the human eyes, so the image acquisition system 200 can be suitably configured to have a horizontally shaped filter area over the sensor. FIG. 34 illustrates another embodiment where the user observes the sensor / lens assembly at an angle and the mirror is inclined such that the eyes are close to the top of the sensor rather than at the center of the sensor. This configuration makes it possible to place an IR blocking filter at one end of the sensor, so that the three regions (non-IR-blocking, IR-blocking and non-IR-blocking), which is the case where the sensor is shown in FIG. As well as having two distinct regions (IR-blocking and non-IR-blocking). This allows a larger and more continuous non-iris part of the scene to be obtained.

35 illustrates another embodiment of an image acquisition system 200 in which an operator may be holding the image acquisition device 200 to acquire an iris image of a user. In this embodiment, there is a see-through guidance channel that the operator can see to line up with the eyes of the user. Additionally or alternatively, spaced guiding markers may be placed on top of the image acquisition device 200, such that the operator lines up the user's eyes, for example using two markers. 36 shows an enlarged view of one embodiment for a guide channel. In this embodiment, as shown, circular rings may be printed on the inner portion of the guide channel before and after the guide channel. When the user is aligned, these rings may appear concentric to the operator. Otherwise they will not be concentric (users' eyes misaligned). 36 also shows visible illuminators (LEDs) on the device, as well as infrared illuminators that can be used for iris recognition purposes. 37 illustrates another embodiment of an image acquisition system. In this embodiment, the LEDs are controlled by controllers, which are connected to the processor, which is also connected to the sensor used for iris recognition.

38 shows one embodiment of a method for capturing images of an iris and a scene using a single image sensor. The image sensor captures 382 a view of the scene and a view of the iris within the at least one image. The image processing module generates 384 an image of the scene by applying a level of noise reduction to the first portion of the at least one image. The image processing module applies the level of reduced noise reduction to the second portion of the at least one image to generate an image of the iris for use in biometric identification (step 386).

With further reference to FIG. 38, more specifically, the image sensor 202 of the image acquisition system 200 captures a view of the scene and a view of the iris within at least one image (382). The image sensor can capture a view of the scene within one image and a view of the iris within another image. In some embodiments, the image sensor can capture a view of the scene and a view of the iris within a single image. For example, the view of the scene may include at least a portion of the iris. The image sensor may capture a view of the scene and a view of the iris within the plurality of images. The image sensor can capture a view of the scene within some images and a view of the iris within other images. The image sensor may capture a view of the scene and a view of the iris within some images. The image sensor may capture two or more images over a period of time. The image sensor may capture two or more images within a short time frame of each other, for example for later comparison or processing. The image sensor may capture two or more images under different conditions, for example with or without infrared illumination, or with or without any type of filter discussed herein.

In some embodiments, image acquisition system 200 may include an iris capturing mode and a picture (eg, non-iris) capturing mode. The image sensor may capture an image of a view of the scene in picture capturing mode. The image sensor may capture an image of a view of the iris in iris capturing mode. In certain embodiments, image acquisition system 200 may perform simultaneous capture of iris and non-iris images in another mode. The user may select a mode for image acquisition through, for example, an application executed in the image acquisition apparatus 200. In some embodiments, the image acquisition system can capture the view of the scene and the view of the iris as separable components within a single image. The image acquisition system may capture a view of the scene and / or a view of the iris using any embodiment and / or combination of interleaved filters, IR blocking filters, IR pass filters, and other types of filters described herein. have.

In some embodiments, the image sensor includes a plurality of sensor nodes of the image sensor. The image sensor may activate a first subset of sensor nodes that are adapted to capture an image of the iris primarily suitable for biometric identification. The image sensor may activate a second subset of sensor nodes that are primarily adapted to capture non-iris images. An IR pass (G + I) filter or other filter (eg, passing G + I) may be applied over the sensor node which is primarily adapted to capture an image of the iris. IR blocking, visible light pass, specific band pass or color filters may be applied over sensor nodes that are primarily adapted to capture non-iris images.

In some embodiments, the image sensor captures at least one image of the iris while illuminating the iris using infrared illumination. The image sensor may capture at least one image of the iris without infrared illumination. The image sensor may capture at least one image of the iris upon turning off the visible light illuminator. The image sensor may capture at least one image of the iris using illumination from the screen of the image acquisition system 200. The image sensor may capture at least one image of the iris when the iris is aligned with a portion of the sensor using a mirror of the image acquisition system 200 for guidance. The image sensor may capture at least one image of the iris when the iris is aligned with a portion of the sensor using a see-through channel and / or markers by the operator.

Referring further to 384, the image processing module may apply the level of noise reduction to the first portion of the at least one image to generate an image of the scene. The image acquisition system 200 may apply noise reduction on the image captured by the image sensor. The image acquisition system 200 may apply noise reduction to images stored in the image acquisition system 200, for example, a storage device or a buffer. Image acquisition system 200 may apply noise reduction, including applying an average or median function or filter over some pixels of the images, eg, a 3x3 pixel window. Image acquisition system 200 may apply noise reduction, including reduction of one or both of time varying and time invariant noise from the captured image. Image acquisition system 200 may process or exclude known defect pixels while performing image processing and / or noise reduction. Image acquisition system 200 may apply noise reduction using an image processing module, which may include one or more image signal processors 206 and / or other processors 208. Image acquisition system 200 may apply noise reduction by identifying, processing, and / or compensating for the presence of systematic noise.

In some embodiments, the image processing module can apply noise reduction on the image captured in the non-iris capture mode. The image processing module may apply the level of noise reduction to portions of the image that are not for iris biometrics, for example portions corresponding to IR cutoff filters. The image processing module may apply noise reduction or filtering on normal or non-iris images. The image processing module may generate an image of the general scene that may be perceived better (eg, to a human) than the image before the noise reduction.

With further reference to 386, the image processing module may apply a level of reduced noise reduction to the second portion of the at least one image to generate an image of the iris for use in biometric identification. In some embodiments, the image processing module may disable noise reduction for the image to be used for iris biometric identification. The image processing module may determine that the noise level does not overpower the captured iris texture. The image processing module may perform iris biometric identification based on raw or raw images captured by the image sensor. The image processing module may perform iris biometric identification based on the image captured by the image sensor after some processing, eg, removal of artifacts, sporadic noise, and / or systematic noise.

In some embodiments, the image processing module may apply a level of reduced noise reduction for the image to be used for iris biometric identification. The image processing module may apply a level of reduced noise reduction on the captured image while in the iris capturing mode. The image processing module may perform noise reduction on systematic and / or sporadic noise. The image processing module may disable noise reduction for unsystematic noise. The image processing module may apply the level of reduced noise reduction for the portion of the extracted image, for example the portion corresponding to the IR pass filter, for iris biometric identification. The image processing module may apply a reduction of systematic noise to the portion of the extracted image, for example the portion corresponding to the IR pass filter, for iris biometric identification.

In some embodiments, image processing module 220 subtracts noise from one image of the iris with noise from another image of the iris. Such subtraction can reduce systematic noise and / or sporadic noise. The image processing module 220 may perform subtraction by aligning the two images side by side. The image processing module 220 may align the two images using common reference points (eg, edges of the shapes). Image processing module 220 may align the two images using pattern recognition / matching, correlation, and / or other algorithms. The image processing module 220 may subtract the noise corresponding to the overlapping portion of the two images. The image processing module 220 may reduce ambient noise in one image using ambient noise from another image. Ambient noise can include signals from ambient light or lighting. Ambient noise can include artifacts from ambient lighting sources or reflections of surrounding objects from the surface of the eye. In some embodiments, image processing module 220 may reduce ambient noise from one image captured in the presence of infrared illumination using ambient noise from another image captured in the absence of infrared illumination. .

In certain embodiments, image processing module 220 may recover infrared components from one or more (G + I) pixels imaged on the sensor node array. The image processing module 220 may subtract the G component from (G + I) using the G intensity value in the neighboring pixel. In some embodiments, image processing module 220 may subtract the G component using the estimated G intensity value. The image processing module 220 may use the estimated G intensity value in the processing of the non-iris (eg, general scene) portion of the image. In some embodiments, image processing module 220 may perform gain or brightness control or adjustment on a portion of the at least one image to generate an image of the iris for use in biometric identification. In some embodiments, the amount of infrared illumination may be insufficient or sub-optimal, such that gain or brightness control or adjustment may improve iris image quality. In certain embodiments, gain or brightness control or adjustment may be more desirable for the addition of infrared illuminators, drawing of power to provide infrared illumination, and / or controlling infrared illumination (eg, under different conditions). have. Since infrared signals are captured by some of the sensor nodes / pixels (eg, in an RGB (G + I) array), compensation through gain or brightness control or adjustment may be appropriate.

Specific embodiments of the methods and systems have been described, and it will now be apparent to one skilled in the art that other embodiments incorporating the inventive concepts may be used. It should be understood that the above-described systems may provide any of such components or multiple of each of these components, which components may be provided on a standalone machine or in some embodiments on multiple machines in a distributed system. . The systems and methods described above may be implemented as a method, apparatus or article of manufacture using software and / or engineering techniques to generate software, firmware, hardware or any combination thereof. In addition, the systems and methods described above may be provided as one or more computer readable programs implemented on or in one or more articles of manufacture. As used herein, the term "article of manufacture" refers to code or logic, firmware, programmable logic, memory devices (eg, embedded in or accessible from one or more computer-readable devices). EEPROM, ROM, PROM, RAM, SRAM, etc.), hardware (e.g., integrated circuit chips, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs, etc.), electronic devices, computer readable nonvolatile storage units (e.g., CD-ROM, floppy disk, hard disk drive, etc.). The article of manufacture can be accessed from a file server providing access to computer readable programs via network transmission lines, wireless transmission media, signals propagating through space, radio waves, infrared signals, and the like. The article of manufacture may be a flash memory card or a magnetic tape. The article of manufacture includes hardware logic, as well as software or programmable code embedded in a computer readable medium executed by a processor. In general, computer readable programs may be implemented in any programming language such as LISP, PERL, C, C ++, C #, PROLOG, or in any byte code language such as JAVA. Software programs may be stored as object code on or in one or more articles of manufacture.

Claims (20)

A method of capturing images of an iris and a scene using a single image sensor,
Capturing, by the image sensor, a view of the scene and a view of the iris within the at least one image;
Applying a first mode of noise reduction to a non-iris portion of the at least one image to produce an image of the scene, the first mode of noise reduction being dependent on systematic noise Noise reduction for non-systematic noise and noise reduction for-; And
Applying a second mode of noise reduction to the iris portion of the at least one image to generate an image of the iris for use in biometric identification, wherein the second mode of noise reduction is noise reduction for systematic noise But does not include noise reduction for non-systematic noise
How to include. The method of claim 1,
Capturing a view of the scene and a view of the iris within the at least one image comprises capturing the view of the scene and the view of the iris as separable components within a single image. The method of claim 1,
Capturing at least one image of the iris while illuminating the iris using infrared illumination. The method of claim 1,
Applying noise reduction comprises applying an averaging or median function. The method of claim 1,
Applying noise reduction includes reducing both time varying noise and time invariant noise from the captured image. The method of claim 1,
Subtracting noise from one image of the iris with noise from another image of the iris. The method of claim 1,
Reducing the ambient noise in one image using ambient noise from another image. The method of claim 1,
Reducing ambient noise from one image captured in the presence of infrared illumination, using ambient noise from another image captured without infrared illumination. The method of claim 1,
Performing gain or brightness control on the iris portion of the at least one image to generate an image of the iris for use in biometric identification. The method of claim 1,
Capturing at least one image includes activating a plurality of sensor nodes of the image sensor, wherein the first subset of sensor nodes is adapted to capture an image of the iris suitable for biometric identification and the sensor And the second subset of nodes is adjusted to capture non-iris images. An apparatus for capturing images of an iris and a scene using a single image sensor,
An image sensor for capturing a view of the scene and a view of the iris within the at least one image; And
A first mode of noise reduction in the non-iris portion of the at least one image to produce an image of the scene, wherein the first mode of noise reduction provides noise reduction for systematic noise and noise reduction for non-systematic noise. A second mode of noise reduction in the iris portion of the at least one image to apply an image, and to generate an image of the iris for use in biometric identification, wherein the second mode of noise reduction reduces noise to systematic noise. Image processing module to apply but not including noise reduction for non-systematic noise
Device comprising a. The method of claim 11,
And the image sensor captures the view of the scene and the view of the iris as separable components within a single image. The method of claim 11,
And an illuminator for illuminating the iris using infrared illumination, wherein the image sensor captures at least one image of the illuminated iris. The method of claim 11,
Noise reduction includes application of an average or median function to the captured image. The method of claim 11,
Noise reduction includes reducing both time varying noise and time invariant noise from the captured image. The method of claim 11,
And the image processing module subtracts noise from one image of the iris with noise from another image of the iris. The method of claim 11,
And the image sensor comprises a complementary metal oxide semiconductor (CMOS) sensor. The method of claim 11,
And said image processing module reduces ambient noise from one image captured in the presence of infrared illumination using ambient noise from another image captured without infrared illumination. The method of claim 11,
And the image processing module performs gain or brightness control on the iris portion of the at least one image to generate an image of the iris for use in biometric identification. The method of claim 11,
The image sensor includes a plurality of sensor nodes, the first subset of sensor nodes being adjusted to capture an image of the iris suitable for biometric identification, and the second subset of sensor nodes capturing a non-iris image. Device adjusted to

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